Abstract

In this paper, a new multi-objective evolutionary algorithm (MOEA) named hybrid MOEA with adaptive multi-population strategy (HMOEA-AMP) is proposed for multi-objective optimization problems (MOPs).In the framework of HMOEA-AMP, the particle swarm optimization and differential evolution are hybridized to guide the exploitation of the Pareto optimal solutions and the exploration of the optimal distribution of the achieved solutions, respectively. Multiple subpopulations are constructed in an adaptive fashion according to a number of scalar subproblems, which are decomposed from a MOP through a set of predefined weight vectors. Comprehensive experiments using a set of benchmark are conducted to investigate the performance of HMOEA-AMP in comparison with several state-of-the-art MOEAs. The experimental results show the advantage of the proposed algorithm.

Notes

Acknowledgments

This study was supported in part by the HKSAR RGC GRF project under Grant No. 712513, by the ITF Innovation and Technology Support Programme under Grant No. ITP/045/13LP, by the National Natural Science Foundation of China (NSFC) under Grant No. 71671032 and No. 71571156, by the Major International Joint Research Project of NSFC under Grant No. 71620107003, and by the Open Project funded by State Key Laboratory of Synthetical Automation for Process Industries under Grant No. PAL-N201505.

Compliance with ethical standards

Conflict of interest

Hongfeng Wang declares that he has no conflict of interest. Yaping Fu declares that he has no conflict of interest. Min Huang declares that she has no conflict of interest. George Huang declares that he has no conflict of interest. Junwei Wang declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.